with open(lab_file, 'rb') as handle:
lab = pickle.load(handle)
with open(lab_file_dev, 'rb') as handle:
lab_dev = pickle.load(handle)
# Network initialization
nnet=MLP(options,inp_dim)
nnet.to(device)
cost=nn.NLLLoss()
# Optimizer initialization
optimizer = optim.SGD(nnet.parameters(), lr=lr, momentum=0.0)
# Seeds initialization
np.random.seed(seed)
torch.manual_seed(seed)
# Batch creation (train)
fea_lst=[]
lab_lst=[]
print("Data Preparation...")
for snt in fea_pase.keys():
if fea_pase[snt].shape[0]-lab[snt].shape[0]!=2:
if fea_pase[snt].shape[0]-lab[snt].shape[0]==3:
fea_lst.append(fea_pase[snt][:-3])
lab_lst.append(lab[snt])
# Computing pase features for test
fea_pase_dev = {}
for snt_id in fea_dev.keys():
fea_pase_dev[snt_id] = pase(fea_dev[snt_id]).detach()
fea_pase_dev[snt_id] = fea_pase_dev[snt_id].view(
fea_pase_dev[snt_id].shape[1],
fea_pase_dev[snt_id].shape[2]).transpose(0, 1)
# Network initialization
nnet = MLP(options, inp_dim)
nnet.to(device)
cost = nn.NLLLoss()
# Optimizer initialization
optimizer = optim.SGD(list(nnet.parameters()) + list(pase.parameters()),
lr=lr,
momentum=0.0)
# Seeds initialization
np.random.seed(seed)
torch.manual_seed(seed)
# Batch creation (train)
fea_lst = []
lab_lst = []
print("Data Preparation...")
for snt in fea_pase.keys():
fea_lst.append(fea_pase[snt])
lab_lst.append(